16 Linear Regression Practice

16.1 Your homework is to watch these videos which are posted under the linear regression header on Brightspace and then do the following homework:

Once you have watched these videos, and you can refer to them as often as you would like, please answer and do the following:

Use linear regression to predict the weight of a woman who is 100 inches tall.
Use linear regression to predict the height of the woman who weighs 200 pounds.
Use linear regression to predict the height of a woman who weighs 5 pounds.
Use linear regression to predict the weight of a woman who is 200 inches tall.
Plot weight on the X axes and height on the y-axes and create a best fit line on your plot.
Plot height on the y-axes and wait on the X axes and create a best fit line on your plot.
Add a another column to the women dataframe called GPA which is these 15 numbers: 1.5,4,2,3.7,4,1, 3, 2.5, 3.8, 0.8, 2, 4, 1, 3, 2.
Use GPA to predict height. Is GPA a significant predictor and how do you know? Draw a best fit line on this relationship.
Use GPA to predict a weight. Is GPA a significant predictor and how do you know? Draw a bested line on this relationship, too.
Predict the height of a person with a GPA of 4.0.

I have posted a short video walking you through how to perform multiple linear regression – where you have more than one variable predicting another.

Using the data set mtcars data set:

Which variable predicts miles per gallon better gear or qsec? How can you tell?
Which two variables out of these four (qsec, vs, am, gear) together best predict miles per gallon?
Using only the number of cylinders, displacement, and weight what would mpg you would you predict for a car with a displacement of 400 inches, eight cylinders, and weighing 2000 pounds?
Be able to explain in a model which variables are significantly significant.
Be able to explain what adjusted R squared means.